Overview

Dataset statistics

Number of variables21
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory328.2 KiB
Average record size in memory168.1 B

Variable types

Numeric14
Categorical7

Warnings

front_camera_megapixels is highly correlated with primary_camera_megapixelsHigh correlation
has_four_g is highly correlated with has_three_gHigh correlation
primary_camera_megapixels is highly correlated with front_camera_megapixelsHigh correlation
pixel_resolution_height is highly correlated with pixel_resolution_widthHigh correlation
pixel_resolution_width is highly correlated with pixel_resolution_heightHigh correlation
ram is highly correlated with price_rangeHigh correlation
screen_height is highly correlated with screen_widthHigh correlation
screen_width is highly correlated with screen_heightHigh correlation
has_three_g is highly correlated with has_four_gHigh correlation
price_range is highly correlated with ramHigh correlation
front_camera_megapixels is highly correlated with primary_camera_megapixelsHigh correlation
has_four_g is highly correlated with has_three_gHigh correlation
primary_camera_megapixels is highly correlated with front_camera_megapixelsHigh correlation
ram is highly correlated with price_rangeHigh correlation
has_three_g is highly correlated with has_four_gHigh correlation
price_range is highly correlated with ramHigh correlation
battery_power is highly correlated with has_dual_sim and 3 other fieldsHigh correlation
has_bluetooth is highly correlated with has_four_g and 2 other fieldsHigh correlation
clock_speed is highly correlated with has_four_g and 2 other fieldsHigh correlation
has_dual_sim is highly correlated with battery_power and 2 other fieldsHigh correlation
front_camera_megapixels is highly correlated with has_four_g and 2 other fieldsHigh correlation
has_four_g is highly correlated with has_bluetooth and 2 other fieldsHigh correlation
internal_memory is highly correlated with has_three_g and 1 other fieldsHigh correlation
depth is highly correlated with has_three_g and 1 other fieldsHigh correlation
weight is highly correlated with has_three_g and 1 other fieldsHigh correlation
number_of_cores is highly correlated with has_three_g and 1 other fieldsHigh correlation
primary_camera_megapixels is highly correlated with has_three_g and 1 other fieldsHigh correlation
pixel_resolution_height is highly correlated with has_three_gHigh correlation
pixel_resolution_width is highly correlated with has_three_g and 1 other fieldsHigh correlation
ram is highly correlated with has_three_g and 1 other fieldsHigh correlation
screen_height is highly correlated with has_three_g and 1 other fieldsHigh correlation
screen_width is highly correlated with has_three_gHigh correlation
talk_time is highly correlated with has_three_g and 1 other fieldsHigh correlation
has_three_g is highly correlated with battery_power and 15 other fieldsHigh correlation
has_touch_screen is highly correlated with battery_power and 8 other fieldsHigh correlation
has_wifi is highly correlated with battery_power and 5 other fieldsHigh correlation
has_three_g is highly correlated with has_four_gHigh correlation
price_range is highly correlated with ramHigh correlation
has_four_g is highly correlated with has_three_gHigh correlation
ram is highly correlated with price_rangeHigh correlation
pixel_resolution_height is highly correlated with pixel_resolution_widthHigh correlation
primary_camera_megapixels is highly correlated with front_camera_megapixelsHigh correlation
screen_width is highly correlated with screen_heightHigh correlation
pixel_resolution_width is highly correlated with pixel_resolution_heightHigh correlation
screen_height is highly correlated with screen_widthHigh correlation
front_camera_megapixels is highly correlated with primary_camera_megapixelsHigh correlation
has_three_g is highly correlated with has_four_gHigh correlation
has_four_g is highly correlated with has_three_gHigh correlation
price_range is uniformly distributed Uniform
front_camera_megapixels has 474 (23.7%) zeros Zeros
primary_camera_megapixels has 101 (5.1%) zeros Zeros
screen_width has 180 (9.0%) zeros Zeros

Reproduction

Analysis started2021-08-31 03:02:08.284480
Analysis finished2021-08-31 03:02:37.835489
Duration29.55 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

battery_power
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1094
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1238.5185
Minimum501
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:37.939646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile570.95
Q1851.75
median1226
Q31615.25
95-th percentile1930.15
Maximum1998
Range1497
Interquartile range (IQR)763.5

Descriptive statistics

Standard deviation439.4182061
Coefficient of variation (CV)0.3547934133
Kurtosis-1.224143883
Mean1238.5185
Median Absolute Deviation (MAD)382
Skewness0.03189847179
Sum2477037
Variance193088.3598
MonotonicityNot monotonic
2021-08-30T23:02:38.089032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18726
 
0.3%
6186
 
0.3%
15896
 
0.3%
17155
 
0.2%
18075
 
0.2%
13105
 
0.2%
10835
 
0.2%
15125
 
0.2%
13795
 
0.2%
19495
 
0.2%
Other values (1084)1947
97.4%
ValueCountFrequency (%)
5012
 
0.1%
5022
 
0.1%
5033
0.1%
5045
0.2%
5061
 
0.1%
5072
 
0.1%
5083
0.1%
5091
 
0.1%
5103
0.1%
5114
0.2%
ValueCountFrequency (%)
19981
 
0.1%
19971
 
0.1%
19962
0.1%
19952
0.1%
19943
0.1%
19931
 
0.1%
19922
0.1%
19914
0.2%
19892
0.1%
19881
 
0.1%

has_bluetooth
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
0
1010 
1
990 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
01010
50.5%
1990
49.5%

Length

2021-08-30T23:02:38.356084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-30T23:02:38.428496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01010
50.5%
1990
49.5%

Most occurring characters

ValueCountFrequency (%)
01010
50.5%
1990
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01010
50.5%
1990
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01010
50.5%
1990
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01010
50.5%
1990
49.5%

clock_speed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct26
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.52225
Minimum0.5
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:38.502050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.7
median1.5
Q32.2
95-th percentile2.8
Maximum3
Range2.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.8160042089
Coefficient of variation (CV)0.5360513772
Kurtosis-1.323417222
Mean1.52225
Median Absolute Deviation (MAD)0.8
Skewness0.1780841203
Sum3044.5
Variance0.6658628689
MonotonicityNot monotonic
2021-08-30T23:02:38.633332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.5413
20.6%
2.885
 
4.2%
2.378
 
3.9%
2.176
 
3.8%
1.676
 
3.8%
2.574
 
3.7%
0.674
 
3.7%
1.470
 
3.5%
1.368
 
3.4%
1.567
 
3.4%
Other values (16)919
46.0%
ValueCountFrequency (%)
0.5413
20.6%
0.674
 
3.7%
0.764
 
3.2%
0.858
 
2.9%
0.958
 
2.9%
161
 
3.0%
1.151
 
2.5%
1.256
 
2.8%
1.368
 
3.4%
1.470
 
3.5%
ValueCountFrequency (%)
328
 
1.4%
2.962
3.1%
2.885
4.2%
2.755
2.8%
2.655
2.8%
2.574
3.7%
2.458
2.9%
2.378
3.9%
2.259
2.9%
2.176
3.8%

has_dual_sim
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1019 
0
981 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
11019
50.9%
0981
49.0%

Length

2021-08-30T23:02:38.883239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-30T23:02:38.954958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
11019
50.9%
0981
49.0%

Most occurring characters

ValueCountFrequency (%)
11019
50.9%
0981
49.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11019
50.9%
0981
49.0%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11019
50.9%
0981
49.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11019
50.9%
0981
49.0%

front_camera_megapixels
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3095
Minimum0
Maximum19
Zeros474
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:39.027997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.341443748
Coefficient of variation (CV)1.007412402
Kurtosis0.2770763246
Mean4.3095
Median Absolute Deviation (MAD)3
Skewness1.019811411
Sum8619
Variance18.84813382
MonotonicityNot monotonic
2021-08-30T23:02:39.137386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0474
23.7%
1245
12.2%
2189
 
9.4%
3170
 
8.5%
5139
 
7.0%
4133
 
6.7%
6112
 
5.6%
7100
 
5.0%
978
 
3.9%
877
 
3.9%
Other values (10)283
14.1%
ValueCountFrequency (%)
0474
23.7%
1245
12.2%
2189
 
9.4%
3170
 
8.5%
4133
 
6.7%
5139
 
7.0%
6112
 
5.6%
7100
 
5.0%
877
 
3.9%
978
 
3.9%
ValueCountFrequency (%)
191
 
0.1%
1811
 
0.5%
176
 
0.3%
1624
 
1.2%
1523
 
1.1%
1420
 
1.0%
1340
2.0%
1245
2.2%
1151
2.5%
1062
3.1%

has_four_g
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1043 
0
957 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Length

2021-08-30T23:02:39.361675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-30T23:02:39.433581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Most occurring characters

ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11043
52.1%
0957
47.9%

internal_memory
Real number (ℝ≥0)

HIGH CORRELATION

Distinct63
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.0465
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:39.528651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q116
median32
Q348
95-th percentile61
Maximum64
Range62
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.14571496
Coefficient of variation (CV)0.5662307882
Kurtosis-1.21607403
Mean32.0465
Median Absolute Deviation (MAD)16
Skewness0.05788932785
Sum64093
Variance329.2669712
MonotonicityNot monotonic
2021-08-30T23:02:39.676967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2747
 
2.4%
1645
 
2.2%
1445
 
2.2%
5742
 
2.1%
242
 
2.1%
4240
 
2.0%
740
 
2.0%
4439
 
1.9%
3039
 
1.9%
637
 
1.8%
Other values (53)1584
79.2%
ValueCountFrequency (%)
242
2.1%
325
1.2%
420
1.0%
536
1.8%
637
1.8%
740
2.0%
837
1.8%
935
1.8%
1036
1.8%
1134
1.7%
ValueCountFrequency (%)
6431
1.6%
6330
1.5%
6221
1.1%
6127
1.4%
6027
1.4%
5918
0.9%
5836
1.8%
5742
2.1%
5627
1.4%
5529
1.5%

depth
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50175
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:39.812274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.5
Q30.8
95-th percentile1
Maximum1
Range0.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.2884155496
Coefficient of variation (CV)0.5748192319
Kurtosis-1.274348884
Mean0.50175
Median Absolute Deviation (MAD)0.3
Skewness0.08908200979
Sum1003.5
Variance0.08318352926
MonotonicityNot monotonic
2021-08-30T23:02:39.909450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.1320
16.0%
0.2213
10.7%
0.8208
10.4%
0.5205
10.2%
0.7200
10.0%
0.3199
10.0%
0.9195
9.8%
0.6186
9.3%
0.4168
8.4%
1106
 
5.3%
ValueCountFrequency (%)
0.1320
16.0%
0.2213
10.7%
0.3199
10.0%
0.4168
8.4%
0.5205
10.2%
0.6186
9.3%
0.7200
10.0%
0.8208
10.4%
0.9195
9.8%
1106
 
5.3%
ValueCountFrequency (%)
1106
 
5.3%
0.9195
9.8%
0.8208
10.4%
0.7200
10.0%
0.6186
9.3%
0.5205
10.2%
0.4168
8.4%
0.3199
10.0%
0.2213
10.7%
0.1320
16.0%

weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct121
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.249
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:40.033518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile86
Q1109
median141
Q3170
95-th percentile196
Maximum200
Range120
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.3996549
Coefficient of variation (CV)0.2524057562
Kurtosis-1.210376474
Mean140.249
Median Absolute Deviation (MAD)31
Skewness0.006558157429
Sum280498
Variance1253.135567
MonotonicityNot monotonic
2021-08-30T23:02:40.189486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18228
 
1.4%
10127
 
1.4%
18527
 
1.4%
14626
 
1.3%
19926
 
1.3%
8825
 
1.2%
19825
 
1.2%
10525
 
1.2%
8924
 
1.2%
13123
 
1.1%
Other values (111)1744
87.2%
ValueCountFrequency (%)
8021
1.1%
8113
0.7%
8215
0.8%
8319
0.9%
8417
0.9%
8513
0.7%
8619
0.9%
8715
0.8%
8825
1.2%
8924
1.2%
ValueCountFrequency (%)
20019
0.9%
19926
1.3%
19825
1.2%
19719
0.9%
19620
1.0%
19511
0.5%
19416
0.8%
19315
0.8%
19215
0.8%
19115
0.8%

number_of_cores
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5205
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:40.315172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.287836718
Coefficient of variation (CV)0.5061025811
Kurtosis-1.229749767
Mean4.5205
Median Absolute Deviation (MAD)2
Skewness0.003627508314
Sum9041
Variance5.234196848
MonotonicityNot monotonic
2021-08-30T23:02:40.724729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4274
13.7%
7259
13.0%
8256
12.8%
2247
12.3%
3246
12.3%
5246
12.3%
1242
12.1%
6230
11.5%
ValueCountFrequency (%)
1242
12.1%
2247
12.3%
3246
12.3%
4274
13.7%
5246
12.3%
6230
11.5%
7259
13.0%
8256
12.8%
ValueCountFrequency (%)
8256
12.8%
7259
13.0%
6230
11.5%
5246
12.3%
4274
13.7%
3246
12.3%
2247
12.3%
1242
12.1%

primary_camera_megapixels
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9165
Minimum0
Maximum20
Zeros101
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:40.840018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.064314941
Coefficient of variation (CV)0.6115378351
Kurtosis-1.171498795
Mean9.9165
Median Absolute Deviation (MAD)5
Skewness0.01730615047
Sum19833
Variance36.77591571
MonotonicityNot monotonic
2021-08-30T23:02:40.959158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10122
 
6.1%
7119
 
5.9%
9112
 
5.6%
20110
 
5.5%
1104
 
5.2%
14104
 
5.2%
0101
 
5.1%
299
 
5.0%
1799
 
5.0%
695
 
4.8%
Other values (11)935
46.8%
ValueCountFrequency (%)
0101
5.1%
1104
5.2%
299
5.0%
393
4.7%
495
4.8%
559
2.9%
695
4.8%
7119
5.9%
889
4.5%
9112
5.6%
ValueCountFrequency (%)
20110
5.5%
1983
4.2%
1882
4.1%
1799
5.0%
1688
4.4%
1592
4.6%
14104
5.2%
1385
4.2%
1290
4.5%
1179
4.0%

pixel_resolution_height
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1137
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean645.108
Minimum0
Maximum1960
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:41.102059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70.95
Q1282.75
median564
Q3947.25
95-th percentile1485.05
Maximum1960
Range1960
Interquartile range (IQR)664.5

Descriptive statistics

Standard deviation443.7808108
Coefficient of variation (CV)0.6879170787
Kurtosis-0.3158654936
Mean645.108
Median Absolute Deviation (MAD)318
Skewness0.6662712561
Sum1290216
Variance196941.408
MonotonicityNot monotonic
2021-08-30T23:02:41.250526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3477
 
0.4%
1796
 
0.3%
3716
 
0.3%
2756
 
0.3%
6745
 
0.2%
2865
 
0.2%
425
 
0.2%
2115
 
0.2%
6495
 
0.2%
3985
 
0.2%
Other values (1127)1945
97.2%
ValueCountFrequency (%)
02
0.1%
11
 
0.1%
21
 
0.1%
32
0.1%
43
0.1%
51
 
0.1%
61
 
0.1%
71
 
0.1%
82
0.1%
91
 
0.1%
ValueCountFrequency (%)
19601
0.1%
19491
0.1%
19201
0.1%
19141
0.1%
19011
0.1%
18991
0.1%
18951
0.1%
18781
0.1%
18741
0.1%
18691
0.1%

pixel_resolution_width
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1109
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1251.5155
Minimum500
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:41.407246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile579.85
Q1874.75
median1247
Q31633
95-th percentile1929.05
Maximum1998
Range1498
Interquartile range (IQR)758.25

Descriptive statistics

Standard deviation432.1994469
Coefficient of variation (CV)0.3453408663
Kurtosis-1.186005229
Mean1251.5155
Median Absolute Deviation (MAD)376
Skewness0.01478747377
Sum2503031
Variance186796.3619
MonotonicityNot monotonic
2021-08-30T23:02:41.554536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8747
 
0.4%
12477
 
0.4%
13836
 
0.3%
14636
 
0.3%
14696
 
0.3%
13935
 
0.2%
17815
 
0.2%
17675
 
0.2%
19235
 
0.2%
14295
 
0.2%
Other values (1099)1943
97.2%
ValueCountFrequency (%)
5002
0.1%
5012
0.1%
5031
 
0.1%
5061
 
0.1%
5074
0.2%
5081
 
0.1%
5092
0.1%
5103
0.1%
5112
0.1%
5122
0.1%
ValueCountFrequency (%)
19981
 
0.1%
19971
 
0.1%
19961
 
0.1%
19953
0.1%
19942
 
0.1%
19921
 
0.1%
19911
 
0.1%
19901
 
0.1%
19893
0.1%
19885
0.2%

ram
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1562
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2124.213
Minimum256
Maximum3998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:41.707355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum256
5-th percentile445
Q11207.5
median2146.5
Q33064.5
95-th percentile3826.35
Maximum3998
Range3742
Interquartile range (IQR)1857

Descriptive statistics

Standard deviation1084.732044
Coefficient of variation (CV)0.5106512594
Kurtosis-1.19191307
Mean2124.213
Median Absolute Deviation (MAD)932.5
Skewness0.006628035399
Sum4248426
Variance1176643.606
MonotonicityNot monotonic
2021-08-30T23:02:41.855359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14644
 
0.2%
31424
 
0.2%
26104
 
0.2%
22274
 
0.2%
12294
 
0.2%
36543
 
0.1%
12773
 
0.1%
10503
 
0.1%
27753
 
0.1%
26743
 
0.1%
Other values (1552)1965
98.2%
ValueCountFrequency (%)
2561
0.1%
2582
0.1%
2591
0.1%
2621
0.1%
2631
0.1%
2651
0.1%
2671
0.1%
2731
0.1%
2771
0.1%
2782
0.1%
ValueCountFrequency (%)
39981
0.1%
39961
0.1%
39931
0.1%
39912
0.1%
39901
0.1%
39841
0.1%
39781
0.1%
39711
0.1%
39702
0.1%
39691
0.1%

screen_height
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.3065
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:41.978161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median12
Q316
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.213245004
Coefficient of variation (CV)0.3423593227
Kurtosis-1.190791247
Mean12.3065
Median Absolute Deviation (MAD)4
Skewness-0.09888424098
Sum24613
Variance17.75143347
MonotonicityNot monotonic
2021-08-30T23:02:42.085869image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
17193
 
9.7%
12157
 
7.8%
7151
 
7.5%
16143
 
7.1%
14143
 
7.1%
15135
 
6.8%
13131
 
6.6%
11126
 
6.3%
10125
 
6.2%
9124
 
6.2%
Other values (5)572
28.6%
ValueCountFrequency (%)
597
4.9%
6114
5.7%
7151
7.5%
8117
5.9%
9124
6.2%
10125
6.2%
11126
6.3%
12157
7.8%
13131
6.6%
14143
7.1%
ValueCountFrequency (%)
19124
6.2%
18120
6.0%
17193
9.7%
16143
7.1%
15135
6.8%
14143
7.1%
13131
6.6%
12157
7.8%
11126
6.3%
10125
6.2%

screen_width
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.767
Minimum0
Maximum18
Zeros180
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:42.199298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.356397606
Coefficient of variation (CV)0.7554010067
Kurtosis-0.3895227894
Mean5.767
Median Absolute Deviation (MAD)3
Skewness0.6337870734
Sum11534
Variance18.9782001
MonotonicityNot monotonic
2021-08-30T23:02:42.315843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1210
10.5%
3199
10.0%
4182
9.1%
0180
9.0%
5161
 
8.1%
2156
 
7.8%
7132
 
6.6%
6130
 
6.5%
8125
 
6.2%
10107
 
5.3%
Other values (9)418
20.9%
ValueCountFrequency (%)
0180
9.0%
1210
10.5%
2156
7.8%
3199
10.0%
4182
9.1%
5161
8.1%
6130
6.5%
7132
6.6%
8125
6.2%
997
4.9%
ValueCountFrequency (%)
188
 
0.4%
1719
 
0.9%
1629
 
1.5%
1531
 
1.6%
1433
 
1.7%
1349
2.5%
1268
3.4%
1184
4.2%
10107
5.3%
997
4.9%

talk_time
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.011
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2021-08-30T23:02:42.434738image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median11
Q316
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.463955198
Coefficient of variation (CV)0.4962269728
Kurtosis-1.218590963
Mean11.011
Median Absolute Deviation (MAD)5
Skewness0.009511762222
Sum22022
Variance29.8548064
MonotonicityNot monotonic
2021-08-30T23:02:42.545299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7124
 
6.2%
4123
 
6.2%
16116
 
5.8%
15115
 
5.8%
19113
 
5.7%
6111
 
5.5%
10105
 
5.2%
8104
 
5.2%
11103
 
5.1%
20102
 
5.1%
Other values (9)884
44.2%
ValueCountFrequency (%)
299
5.0%
394
4.7%
4123
6.2%
593
4.7%
6111
5.5%
7124
6.2%
8104
5.2%
9100
5.0%
10105
5.2%
11103
5.1%
ValueCountFrequency (%)
20102
5.1%
19113
5.7%
18100
5.0%
1798
4.9%
16116
5.8%
15115
5.8%
14101
5.1%
13100
5.0%
1299
5.0%
11103
5.1%

has_three_g
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1523 
0
477 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11523
76.1%
0477
 
23.8%

Length

2021-08-30T23:02:42.769154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-30T23:02:42.845708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
11523
76.1%
0477
 
23.8%

Most occurring characters

ValueCountFrequency (%)
11523
76.1%
0477
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11523
76.1%
0477
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11523
76.1%
0477
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11523
76.1%
0477
 
23.8%

has_touch_screen
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1006 
0
994 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Length

2021-08-30T23:02:43.032428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-30T23:02:43.104120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Most occurring characters

ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11006
50.3%
0994
49.7%

has_wifi
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1014 
0
986 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
11014
50.7%
0986
49.3%

Length

2021-08-30T23:02:43.299531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-30T23:02:43.372824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
11014
50.7%
0986
49.3%

Most occurring characters

ValueCountFrequency (%)
11014
50.7%
0986
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11014
50.7%
0986
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11014
50.7%
0986
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11014
50.7%
0986
49.3%

price_range
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
500 
2
500 
3
500 
0
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1500
25.0%
2500
25.0%
3500
25.0%
0500
25.0%

Length

2021-08-30T23:02:43.564494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-30T23:02:43.640445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1500
25.0%
2500
25.0%
3500
25.0%
0500
25.0%

Most occurring characters

ValueCountFrequency (%)
1500
25.0%
2500
25.0%
3500
25.0%
0500
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1500
25.0%
2500
25.0%
3500
25.0%
0500
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1500
25.0%
2500
25.0%
3500
25.0%
0500
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1500
25.0%
2500
25.0%
3500
25.0%
0500
25.0%

Interactions

2021-08-30T23:02:11.000195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:11.145590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:11.271370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:11.404472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:11.531605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:11.659584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:11.789166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:11.920116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:12.054166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:12.192902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:12.324605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:12.453235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:12.579774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:12.713356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:12.849006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:12.972116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:13.088718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:13.218299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:13.336257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:13.453171image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:13.571942image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:13.789983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:13.921765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:14.048059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:14.174802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:14.297797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:14.418089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:14.541037image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:14.666529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:14.801348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:14.926165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:15.058001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:15.188727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:15.312915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:15.439790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:15.569169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:15.706610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:15.842453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:15.974267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:16.103194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:16.234786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:16.366496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:16.495761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:16.619385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:16.744287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:16.869866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:16.991662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:17.107721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:17.346235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:17.478139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:17.609995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:17.741522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:17.869129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:17.992713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:18.113163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:18.239893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:18.367064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:18.485122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:18.596037image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:18.717155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:18.837739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:18.944667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:19.059242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:19.179188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:19.304344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:19.426158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:19.545134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:19.666644image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:19.786562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:19.907563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:20.026100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:20.152222image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:20.273975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:20.401515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:20.524425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:20.638744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:20.761278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:20.885706image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:21.015976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:21.143702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:21.272624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:21.544187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:21.675182image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:21.805172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:21.932183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:22.064759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:22.191211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:22.321021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:22.449544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:22.569791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:22.697700image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:22.823095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:22.957768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:23.095601image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:23.227887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:23.355354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:23.484066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:23.613090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:23.744545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:23.881569image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:24.014127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:24.149950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:24.294107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:24.420444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:24.553569image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:24.690976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:24.829625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:24.968455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:25.104987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:25.241945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:25.372522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:25.509531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:25.642637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:25.779348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:25.906701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:26.045073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:26.175236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:26.302338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:26.432555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:26.569181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:26.897960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:27.045990image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:27.188150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:27.324497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:27.453166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:27.587950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:27.722478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:27.852687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:27.977599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:28.111330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:28.242488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:28.366618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:28.494620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:28.624159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:28.760465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:28.893598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:29.032520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:29.166270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:29.303787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:29.434715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:29.566510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:29.697719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:29.823041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:29.952819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:30.080013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:30.199915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:30.328488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:30.454445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:30.589333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:30.727121image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:30.857267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:30.984092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:31.117655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:31.263985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:31.405107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:31.534478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:31.654888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:31.783364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:31.904995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:32.020511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:32.142670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:32.268794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:32.399682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:32.528181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:32.655655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:32.782229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:32.901654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:33.026994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:33.395954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:33.532366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:33.658620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:33.792669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:33.918356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:34.039952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:34.169699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:34.299578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:34.434825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:34.580823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:34.716052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:34.846136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:34.972991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:35.103795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:35.235214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:35.378902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:35.514605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:35.646745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:35.778950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:35.898007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:36.024829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:36.160005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:36.306711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:36.439585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:36.574250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:36.705309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:36.834289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-30T23:02:36.962545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-30T23:02:43.755081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-30T23:02:44.043950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-30T23:02:44.329290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-30T23:02:44.618393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-30T23:02:44.862170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-30T23:02:37.251339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-30T23:02:37.682539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

battery_powerhas_bluetoothclock_speedhas_dual_simfront_camera_megapixelshas_four_ginternal_memorydepthweightnumber_of_coresprimary_camera_megapixelspixel_resolution_heightpixel_resolution_widthramscreen_heightscreen_widthtalk_timehas_three_ghas_touch_screenhas_wifiprice_range
084202.201070.61882220756254997190011
1102110.5101530.7136369051988263117371102
256310.5121410.91455612631716260311291102
361512.5000100.813169121617862769168111002
4182111.20131440.614121412081212141182151101
5185900.5130220.716417100416541067171101001
6182101.7041100.813981038110183220138181013
7195400.5100240.818740512114970016351110
8144510.5000530.71747143868361099171201000
950910.612190.193515113712245131910121000

Last rows

battery_powerhas_bluetoothclock_speedhas_dual_simfront_camera_megapixelshas_four_ginternal_memorydepthweightnumber_of_coresprimary_camera_megapixelspixel_resolution_heightpixel_resolution_widthramscreen_heightscreen_widthtalk_timehas_three_ghas_touch_screenhas_wifiprice_range
1990161712.4081360.8851974314262965371000
1991188202.00111440.811381947433579198201103
199267412.9110210.219834576180911806341110
1993146710.5000180.61225088810993962151151113
199485802.2010500.1841252814163978171631103
199579410.510120.810661412221890668134191100
1996196512.6100390.218743915196520321110161112
1997191100.9111360.710883868163230579151103
1998151200.9041460.1145553366708691810191110
199951012.0151450.9168616483754391919421113